A density based clustering library that takes distance matrix as input

Question

Need help with finding an open/free density based clustering library that takes a distance matrix as input and returns clusters with each element within it maximum "x" distance away from each of the other elements in the clusters (basically returning clusters with specified density).

I checked out the DBSCAN algorithm, it seems to suit my needs. Any clean implementations of DBSCAN that you might no off, which can take off with a pre-computed distance matrix and output clusters with the desired density?

Your inputs will be really useful.

Answer 1

ELKI (at http://elki.dbs.ifi.lmu.de/ ) can load external distance matrixes, either in a binary or an Ascii format and then run distance-based clustering algorithms on it.

Certain algorithms such as k-means cannot work however, as these rely on the distance to the /mean/, which is obviously not precomputed. But e.g. DBSCAN and OPTICS work fine with precomputed distances.

Answer 2

I haven't tried it out yet, but I'm looking for something similar and came across this python implementation of DBSCAN:

http://scikit-learn.org/dev/auto_examples/cluster/plot_dbscan.html#example-cluster-plot-dbscan-py

Answer 3

Matlab file exchange has an implementation which is straightforward to adapt to precomputed matrices. Just remove the call to pdist1 outside the function in your code.